Biology subjects

Krantz, M.

Publications and source records attributed to Krantz, M..

5 recordsLinked to original sources

bioRxiv2023

A detailed Molecular Network Map and Model of the NLRP3 Inflammasome

The NLRP3 inflammasome is a key regulator of inflammation that responds to a broad range of stimuli. The exact mechanism of activation has not been determined, but there is a consensus on cellular potassium efflux as a major common denominator. Once NLRP3 is activated, it forms high-order complexes together with NEK7 that trigger aggregation of ASC into specks. Typically, there is only one speck per cell, consistent with the proposal that specks form - or end up at - the centrosome. ASC polymerisation in turn triggers caspase-1 activation, leading to maturation and release of IL-1{beta} and pyroptosis, i.e., highly inflammatory cell death. Several gain-of-function mutations in the NLRP3 inflammasome have been suggested to induce spontaneous activation of NLRP3 and hence contribute to development and disease severity in numerous autoinflammatory and autoimmune diseases. Consequently, the NLRP3 inflammasome is of significant clinical interest, and recent attention has drastically improved our insight in the range of involved triggers and mechanisms of signal transduction. However, despite recent progress in knowledge, a clear and comprehensive overview of how these mechanisms interplay to shape the system level function is missing from the literature. Here, we provide such an overview as a resource to researchers working in or entering the field, as well as a computational model that allows for evaluating and explaining the function of the NLRP3 inflammasome system from the current molecular knowledge. We present a detailed reconstruction of the molecular network surrounding the NLRP3 inflammasome, which account for each specific reaction and the known regulatory constraints on each event as well as the mechanisms of drug action and impact of genetics when known. Furthermore, an executable model from this network reconstruction is generated with the aim to be used to explain NLRP3 activation from priming and activation to the maturation and release of IL-1{beta} and IL-18. Finally, we test this detailed mechanistic model against data on the effect of different modes of inhibition of NLRP3 assembly. While the exact mechanisms of NLRP3 activation remains elusive, the literature indicates that the different stimuli converge on a single activation mechanism that is additionally controlled by distinct (positive or negative) priming and licensing events through covalent modifications of the NLRP3 molecule. Taken together, we present a compilation of the literature knowledge on the molecular mechanisms on NLRP3 activation, a detailed mechanistic model of NLRP3 activation, and explore the convergence of diverse NLRP3 activation stimuli into a single input mechanism.

immunology↗

bioRxiv2019

Impact of small RNAs in retrograde signalling pathways in Arabidopsis thaliana

Chloroplast perturbations activate retrograde signalling pathways causing dynamic changes of gene expression. Besides transcriptional control of gene expression different classes of small non-coding RNAs (sRNAs) act in gene expression control, but comprehensive analyses regarding their role in retrograde signalling is lacking. We performed sRNA profiling in response to norflurazon (NF) that provokes retrograde signals in A. thaliana wild type and the two retrograde signalling mutants gun1 and gun5. The RNA samples were also used for mRNA and long non-coding RNA (lncRNA) profiling to link altered sRNA levels to changes of their cognate target RNAs. We identified 122 sRNAs from all known sRNA classes that were responsive to NF in wild type. Strikingly, 140 and 213 sRNAs were found to be differentially regulated in both mutants indicating a retrograde control of these sRNAs. Concomitant with the changes in sRNA expression we detected about 1500 differentially expressed mRNAs in the NF treated wild type and around 900 and 1400 mRNAs that were differentially regulated in the gun1 and gun5 mutant with a high proportion (~30%) of genes encoding plastid proteins. Furthermore, around 20% of predicted miRNA targets code for plastid localised proteins. The analyses of sRNA-target pairs identified pairs with an anticorrelated expression as well pairs showing other expressional relations pointing to a role of sRNAs in balancing transcriptional changes upon retrograde signals. Based on the comprehensive changes in sRNA expression we assume a considerable impact of sRNAs in retrograde-dependent transcriptional changes to adjust plastidic and nuclear gene expression. Significance statementPerturbations of plastid functions trigger retrograde signalling to adjust plastidic and nuclear gene expression, however, the role of small non-coding RNAs acting as regulators in these pathways is not well understood. We analysed small non-coding RNA expression in response to retrograde signals in A. thaliana wild type and two retrograde signalling mutants and identified members of all known small non-coding RNA classes pointing to a functional role of these RNA classes in retrograde pathways.

plant biology↗

bioRxiv2018

A comprehensive, mechanistically detailed, and executable model of the Cell Division Cycle in Saccharomyces cerevisiae

Understanding how cellular functions emerge from the underlying molecular mechanisms is a key challenge in biology. This will require computational models, whose predictive power is expected to increase with coverage and precision of formulation. Genome-scale models revolutionised the metabolic field and made the first whole-cell model possible. However, the lack of genome-scale models of signalling networks blocks the development of eukaryotic whole-cell models. Here, we present a comprehensive mechanistic model of the molecular network that controls the cell division cycle in Saccharomyces cerevisiae. We use rxncon, the reaction-contingency language, to neutralise the scalability issues preventing formulation, visualisation and simulation of signalling networks at the genome-scale. We use parameter-free modelling to validate the network and to predict genotype-to-phenotype relationships down to residue resolution. This mechanistic genome-scale model offers a new perspective on eukaryotic cell cycle control, and opens up for similar models - and eventually whole-cell models - of human cells.

systems biology↗

bioRxiv2017

Bipartite Boolean modelling - a method for mechanistic simulation and validation of large-scale signal transduction networks

The metabolic modelling community has established the gold standard for bottom-up systems biology with reconstruction, validation and simulation of mechanistic genome-scale models. Similar methods have not been established for signal transduction networks, where the representation of internal states leads to scalability issues in both model formulation and execution. While rule- and agent-based methods allow efficient model definition and execution, respectively, model parametrisation introduces an additional layer of uncertainty due to the sparsity of reliably measured parameters. Here, we present a scalable method for parameter-free simulation of mechanistic signal transduction networks. It is based on rxncon and uses a bipartite Boolean logic with separate update rules for reactions and states. Using two generic update rules, we enable translation of any rxncon model into a unique Boolean model, which can be used for network validation and simulation - allowing the prediction of system level function directly from molecular mechanistic data. Through scalable model definition and simulation, and the independence of quantitative parameters, it opens up for simulation and validation of mechanistic genome-scale models of signal transduction networks.

systems biology↗

bioRxiv2017

rxncon 2.0: a language for executable molecular systems biology

Large-scale knowledge bases and models become increasingly important to systematise and interpret empirical knowledge on cellular systems. In signalling networks, as opposed to metabolic networks, distinct modifications of and bonds between components combine into very large numbers of possible configurations, or microstates. These are essentially never measured in vivo, making explicit modelling strategies both impractical and problematic. Here, we present rxncon 2.0, the second generation rxncon language, as a tool to define signal transduction networks at the level of empirical data. By expressing both reactions and contingencies (contextual constraints on reactions) in terms of elemental states, both the combinatorial complexity and the discrepancy to empirical data can be minimised. It works as a higher-level language natural to biologists, which can be compiled into a range of graphical formats or executable models. Taken together, the rxncon language combines mechanistic precision with scalability in a composable and compilable language, that is designed for building executable knowledge bases on the molecular biology of signalling systems.

systems biology↗